Hybrid Random Fields: A Scalable Approach to Structure and Parameter Learning in Probabilistic Graphical Models - Couverture souple
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Synopsis
This book presents an exciting new synthesis of directed and undirected, discrete and continuous graphical models. Combining elements of Bayesian networks and Markov random fields, the newly introduced hybrid random fields are an interesting approach to get the best of both these worlds, with an added promise of modularity and scalability. The authors have written an enjoyable book---rigorous in the treatment of the mathematical background, but also enlivened by interesting and original historical and philosophical perspectives.
-- Manfred Jaeger, Aalborg Universitet
The book not only marks an effective direction of investigation with significant experimental advances, but it is also---and perhaps primarily---a guide for the reader through an original trip in the space of probabilistic modeling. While digesting the book, one is enriched with a very open view of the field, with full of stimulating connections. [...] Everyone specifically interested in Bayesian networks and Markov random fields should not miss it.
-- Marco Gori, Università degli Studi di Siena
Graphical models are sometimes regarded---incorrectly---as an impractical approach to machine learning, assuming that they only work well for low-dimensional applications and discrete-valued domains. While guiding the reader through the major achievements of this research area in a technically detailed yet accessible way, the book is concerned with the presentation and thorough (mathematical and experimental) investigation of a novel paradigm for probabilistic graphical modeling, the hybrid random field. This model subsumes and extends both Bayesian networks and Markov random fields. Moreover, it comes with well-defined learning algorithms, both for discrete and continuous-valued domains, which fit the needs of real-world applications involving large-scale, high-dimensional data.
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Hybrid Random Fields (eng)
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Hybrid Random Fields
Edité par
Springer Berlin Heidelberg Jul 2013, 2013
ISBN 10 : 3642268188
ISBN 13 : 9783642268182
Neuf
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents an exciting new synthesis of directed and undirected, discrete and continuous graphical models. Combining elements of Bayesian networks and Markov random fields, the newly introduced hybrid random fields are an interesting approach to get the best of both these worlds, with an added promise of modularity and scalability. The authors have written an enjoyable book---rigorous in the treatment of the mathematical background, but also enlivened by interesting and original historical and philosophical perspectives.-- Manfred Jaeger, Aalborg UniversitetThe book not only marks an effective direction of investigation with significant experimental advances, but it is also---and perhaps primarily---a guide for the reader through an original trip in the space of probabilistic modeling. While digesting the book, one is enriched with a very open view of the field, with full of stimulating connections. [.] Everyone specifically interested in Bayesian networks and Markov random fields should not miss it.-- Marco Gori, Università degli Studi di SienaGraphical models are sometimes regarded---incorrectly---as an impractical approach to machine learning, assuming that they only work well for low-dimensional applications and discrete-valued domains. While guiding the reader through the major achievements of this research area in a technically detailed yet accessible way, the book is concerned with the presentation and thorough (mathematical and experimental) investigation of a novel paradigm for probabilistic graphical modeling, the hybrid random field. This model subsumes and extends both Bayesian networks and Markov random fields. Moreover, it comes with well-defined learning algorithms, both for discrete and continuous-valued domains, which fit the needs of real-world applications involving large-scale, high-dimensional data. 228 pp. Englisch. N° de réf. du vendeur 9783642268182
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Hybrid Random Fields
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Hybrid Random Fields
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Covers the concepts and techniques related to the hybrid random field model for the first time Offers a self-contained introduction to semiparametric and nonparametric density estimation Written by leading experts in the fieldThi. N° de réf. du vendeur 5054862
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Hybrid Random Fields
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Etat : New. Print on Demand pp. 228 49:B&W 6.14 x 9.21 in or 234 x 156 mm (Royal 8vo) Perfect Bound on White w/Gloss Lam. N° de réf. du vendeur 96337981
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Hybrid Random Fields
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Hybrid Random Fields: A Scalable Approach to Structure and Parameter Learning in Probabilistic Graphical Models
Edité par
Springer-Verlag New York Inc, 2013
ISBN 10 : 3642268188
ISBN 13 : 9783642268182
Neuf
Paperback
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Paperback. Etat : Brand New. 2011 edition. 228 pages. 9.13x6.06x0.55 inches. In Stock. N° de réf. du vendeur x-3642268188
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Hybrid Random Fields | A Scalable Approach to Structure and Parameter Learning in Probabilistic Graphical Models
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Taschenbuch. Etat : Neu. Hybrid Random Fields | A Scalable Approach to Structure and Parameter Learning in Probabilistic Graphical Models | Antonino Freno (u. a.) | Taschenbuch | xviii | Englisch | 2013 | Springer | EAN 9783642268182 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. N° de réf. du vendeur 105734340
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Hybrid Random Fields
Edité par
Springer Berlin Heidelberg, Springer Berlin Heidelberg Jul 2013, 2013
ISBN 10 : 3642268188
ISBN 13 : 9783642268182
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book presents an exciting new synthesis of directed and undirected, discrete and continuous graphical models. Combining elements of Bayesian networks and Markov random fields, the newly introduced hybrid random fields are an interesting approach to get the best of both these worlds, with an added promise of modularity and scalability. The authors have written an enjoyable book---rigorous in the treatment of the mathematical background, but also enlivened by interesting and original historical and philosophical perspectives.-- Manfred Jaeger, Aalborg Universitet The book not only marks an effective direction of investigation with significant experimental advances, but it is also---and perhaps primarily---a guide for the reader through an original trip in the space of probabilistic modeling. While digesting the book, one is enriched with a very open view of the field, with full of stimulating connections. [.] Everyone specifically interested in Bayesian networks and Markov random fields should not miss it. -- Marco Gori, Università degli Studi di Siena Graphical models are sometimes regarded---incorrectly---as an impractical approach to machine learning, assuming that they only work well for low-dimensional applications and discrete-valued domains. While guiding the reader through the major achievements of this research area in a technically detailed yet accessible way, the book is concerned with the presentation and thorough (mathematical and experimental) investigation of a novel paradigm for probabilistic graphical modeling, the hybrid random field. This model subsumes and extends both Bayesian networks and Markov random fields. Moreover, it comes with well-defined learning algorithms, both for discrete and continuous-valued domains, which fit the needs of real-world applications involving large-scale, high-dimensional data.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 228 pp. Englisch. N° de réf. du vendeur 9783642268182
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Hybrid Random Fields : A Scalable Approach to Structure and Parameter Learning in Probabilistic Graphical Models
Edité par
Springer Berlin Heidelberg, 2013
ISBN 10 : 3642268188
ISBN 13 : 9783642268182
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents an exciting new synthesis of directed and undirected, discrete and continuous graphical models. Combining elements of Bayesian networks and Markov random fields, the newly introduced hybrid random fields are an interesting approach to get the best of both these worlds, with an added promise of modularity and scalability. The authors have written an enjoyable book---rigorous in the treatment of the mathematical background, but also enlivened by interesting and original historical and philosophical perspectives.-- Manfred Jaeger, Aalborg UniversitetThe book not only marks an effective direction of investigation with significant experimental advances, but it is also---and perhaps primarily---a guide for the reader through an original trip in the space of probabilistic modeling. While digesting the book, one is enriched with a very open view of the field, with full of stimulating connections. [.] Everyone specifically interested in Bayesian networks and Markov random fields should not miss it.-- Marco Gori, Università degli Studi di SienaGraphical models are sometimes regarded---incorrectly---as an impractical approach to machine learning, assuming that they only work well for low-dimensional applications and discrete-valued domains. While guiding the reader through the major achievements of this research area in a technically detailed yet accessible way, the book is concerned with the presentation and thorough (mathematical and experimental) investigation of a novel paradigm for probabilistic graphical modeling, the hybrid random field. This model subsumes and extends both Bayesian networks and Markov random fields. Moreover, it comes with well-defined learning algorithms, both for discrete and continuous-valued domains, which fit the needs of real-world applications involving large-scale, high-dimensional data. N° de réf. du vendeur 9783642268182
Acheter neuf
EUR 106,99
Expédition à EUR 61,77
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